Update.

diff --git a/grids.py b/grids.py
index 03da16d..03fa375 100755 (executable)
--- a/grids.py
+++ b/grids.py
@@ -579,40 +579,52 @@ class Grids(problem.Problem):
             X[i, j] = c[1]
             f_X[0:2, 0:2] = c[1]
 
-    def task_islands(self, A, f_A, B, f_B):
+    def task_symbols(self, A, f_A, B, f_B):
+        nb_rec = 4
+        c = torch.randperm(len(self.colors) - 1)[: nb_rec + 1] + 1
+        delta = 3
         for X, f_X in [(A, f_A), (B, f_B)]:
-            nb_on_border = 0
-            for _ in range(10):
-                for k in torch.randperm(self.height * self.width):
-                    i, j = k % self.height, k // self.height
-                    border = (
-                        i == 0 or i == self.height - 1 or j == 0 or j == self.width - 1
-                    )
-                    no, nq, nq_diag = self.contact(X, i, j, 1)
-
-                    if (
-                        (nq > 0 and not border)
-                        or (nq == 0 and border and nb_on_border < 4)
-                    ) and nq_diag == 0:
-                        X[i, j] = 1
-                        if border:
-                            nb_on_border += 1
-
             while True:
-                nb_fixes = 0
-                for i in range(1, self.height - 1):
-                    for j in range(1, self.width - 1):
-                        if (
-                            X[i, j] == 1
-                            and X[i - 1, j] + X[i + 1, j] + X[i, j - 1] + X[i, j + 1]
-                            == 1
-                        ):
-                            X[i, j] = 0
-                            nb_fixes += 1
-
-                if nb_fixes == 0:
+                i, j = torch.randint(self.height - delta + 1, (nb_rec,)), torch.randint(
+                    self.width - delta + 1, (nb_rec,)
+                )
+                d = (i[None, :] - i[:, None]).abs().max((j[None, :] - j[:, None]).abs())
+                d.fill_diagonal_(delta + 1)
+                if d.min() > delta:
                     break
 
+            for k in range(1, nb_rec):
+                X[i[k] : i[k] + delta, j[k] : j[k] + delta] = c[k]
+
+            ai, aj = i.float().mean(), j.float().mean()
+
+            q = torch.randint(3, (1,)) + 1
+
+            X[i[0] + delta // 2 - 1, j[0] + delta // 2 - 1] = c[0]
+            X[i[0] + delta // 2 - 1, j[0] + delta // 2 + 1] = c[0]
+            X[i[0] + delta // 2 + 1, j[0] + delta // 2 - 1] = c[0]
+            X[i[0] + delta // 2 + 1, j[0] + delta // 2 + 1] = c[0]
+
+            assert i[q] != ai and j[q] != aj
+
+            X[
+                i[0] + delta // 2 + (i[q] - ai).sign().long(),
+                j[0] + delta // 2 + (j[q] - aj).sign().long(),
+            ] = c[nb_rec]
+
+            f_X[i[0] : i[0] + delta, j[0] : j[0] + delta] = c[q]
+
+    def task_islands(self, A, f_A, B, f_B):
+        pass
+
+    # for X, f_X in [(A, f_A), (B, f_B)]:
+    # n = torch.arange(self.height * self.width).reshape(self.height, self.width)
+    # k = torch.randperm(self.height * self.width)
+    # X[...]=-1
+    # for q in k:
+    # i,j=q%self.height,q//self.height
+    # if
+
     ######################################################################
 
     def all_tasks(self):
@@ -627,7 +639,8 @@ class Grids(problem.Problem):
             self.task_trajectory,
             self.task_bounce,
             self.task_scale,
-            # self.task_islands,
+            self.task_symbols,
+            self.task_islands,
         ]
 
     def generate_prompts_and_answers(self, nb, tasks=None, device="cpu"):